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Automatic Generation and Selection of Streamlined Constraint Models via Monte Carlo Search on a Model Lattice

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Principles and Practice of Constraint Programming (CP 2018)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 11008))

Abstract

Streamlined constraint reasoning is the addition of uninferred constraints to a constraint model to reduce the search space, while retaining at least one solution. Previously it has been established that it is possible to generate streamliners automatically from abstract constraint specifications in Essence and that effective combinations of streamliners can allow instances of much larger scale to be solved. A shortcoming of the previous approach was the crude exploration of the power set of all combinations using depth and breadth first search. We present a new approach based on Monte Carlo search over the lattice of streamlined models, which efficiently identifies effective streamliner combinations.

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Notes

  1. 1.

    Streamlining is unsuitable for unsatisfiable problems: streamliners are not necessarily sound, so exhausting the search space does not prove unsatisfiability (a case split approach is possible: a sub-problem with a streamliner, another with its negation).

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Acknowledgements

This work was supported via EPSRC EP/P015638/1. We thank our anonymous reviewers for helpful comments.

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Authors and Affiliations

  1. School of Computer Science, University of St Andrews, St Andrews, UK

    Patrick Spracklen, Özgür Akgün & Ian Miguel

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  1. Patrick Spracklen
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  2. Özgür Akgün
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  3. Ian Miguel
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Correspondence to Özgür Akgün .

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  1. Carnegie Mellon University, Pittsburgh, PA, USA

    John Hooker

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Spracklen, P., Akgün, Ö., Miguel, I. (2018). Automatic Generation and Selection of Streamlined Constraint Models via Monte Carlo Search on a Model Lattice. In: Hooker, J. (eds) Principles and Practice of Constraint Programming. CP 2018. Lecture Notes in Computer Science(), vol 11008. Springer, Cham. https://doi.org/10.1007/978-3-319-98334-9_24

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  • DOI: https://doi.org/10.1007/978-3-319-98334-9_24

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